Fix typo in spilling documentation (#1384)

Small follow-up to #1383

- Fixes a typo in a link that references the "Spilling from device" page
- Small tweaks to the spilling discussion on the "best practices" page

Authors:
  - Richard (Rick) Zamora (https://github.com/rjzamora)

Approvers:
  - Peter Andreas Entschev (https://github.com/pentschev)

URL: #1384

docs/source/examples/best-practices.rst

@@ -49,8 +49,9 @@ Spilling from Device
 
 Dask-CUDA offers several different ways to enable automatic spilling from device memory.
 The best method often depends on the specific workflow. For classic ETL workloads using
-`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, cuDF spilling is usually the
-best place to start. See :ref:`Spilling from device <spilling-from-device>` for more details.
+`Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, native cuDF spilling is usually
+the best place to start. See :ref:`Dask-CUDA's spilling documentation <spilling-from-device>`
+for more details.
 
 Accelerated Networking
 ~~~~~~~~~~~~~~~~~~~~~~

docs/source/spilling.rst

@@ -114,7 +114,7 @@ cuDF Spilling
 
 When executing an ETL workflow with `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_
 (i.e. Dask DataFrame), it is usually best to leverage `native spilling support in cuDF
-<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`.
+<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`_.
 
 Native cuDF spilling has an important advantage over the other methodologies mentioned
 above. When JIT-unspill or default spilling are used, the worker is only able to spill